n-Grams and their implication to natural language understanding
Pattern Recognition
Dr. Dobb's Journal
Off-line handwritten Chinese character recognition as a compound Bayes decision problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fuzzy c-means variant for the generation of fuzzy term sets
Fuzzy Sets and Systems - Theme: Modeling and learning
A Study on Utilizing OCR Technology in Building Text Database
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Robust OCR of Degraded Documents
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
On the interaction between true source, training, and testing language models
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Contextual Postprocessing System for Cooperation with a Multiple-Choice Character-Recognition System
IEEE Transactions on Computers
A signal detection system based on Dempster-Shafer theory andcomparison to fuzzy detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A framework for fuzzy recognition technology
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Postprocessing statistical language models for handwritten Chinesecharacter recognizer
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy modeling for intelligent decision making under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparative study on heuristic algorithms for generating fuzzydecision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neuro-fuzzy inference engine for Farsi numeral characters recognition
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Statistical language models are very useful tools to improve the recognition accuracy of optical character recognition (OCR) systems. In previous systems, segmentation by maximum word matching, semantic class segmentation, or trigram language models have been used. However, these methods have some disadvantages, such as inaccuracies due to a preference for longer words (which may be erroneous), failure to recognize word dependencies, complex semantic training data segmentation, and a requirement of high memory. To overcome these problems, we propose a novel bigram Markov language model in this paper. This type of model does not have large word preferences and does not require semantically segmented training data. Furthermore, unlike trigram models, the memory requirement is small. Thus, the scheme is suitable for handheld and pocket computers, which are expected to be a major future application of text recognition systems. However, due to a simple language model, the bigram Markov model alone can introduce more errors. Hence in this paper, a novel algorithm combining bigram Markov language models with heuristic fuzzy rules is described. It is found that the recognition accuracy is improved through the use of the algorithm, and it is well suited to mobile and pocket computer applications, including as we will show in the experimental results, the ability to run on mobile phones. The main contribution of this paper is to show how fuzzy techniques as linguistic rules can be used to enhance the accuracy of a crisp recognition system, and still have low computational complexity.